A tire uniformity detection signal correction method and system

Abstract

The application discloses a kind of tire uniformity detection signal correction method and system, it is related to tire uniformity detection technical field, comprising: acquisition tire in the original detection signal in uniformity detection process, calculate and determine the sampling point number of tire and load wheel rotation one round;The original detection signal model formed by tire error signal, load wheel error signal and direct current bias superposition is constructed, unknown each superposition component in model is formed into vector form, and matrix is structured to establish linear equation group;According to original detection signal, tire error signal and load wheel error signal initial periodic reference waveform are calculated using periodic synchronous average method, and initial periodic reference waveform is used as initial value, least square method is used to alternately optimize iterative solution of linear equation group, until iteration converges, output load wheel error signal and tire error signal after separating load wheel geometric error.The application can realize the accurate separation of load wheel geometric error in original detection signal.

Description

Technical Field

[0001] This invention relates to the field of tire uniformity detection technology, and in particular to a tire uniformity detection signal correction method and system. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Tire uniformity is one of the core indicators for evaluating tire quality, directly affecting vehicle stability, safety, and comfort. Currently, tire uniformity testing requires loading a load wheel onto the tire and driving both to rotate synchronously, collecting signals such as radial force and lateral force to analyze the tire's geometric and mechanical uniformity characteristics. In actual testing scenarios, the load wheel, as the core moving component for testing, inevitably exhibits geometric non-circular defects such as surface unevenness, roundness deviation, and coaxiality error due to manufacturing processes and wear. These inherent errors are superimposed on the tire's own uniformity signals, masking the tire's true performance characteristics and directly affecting the accuracy of the test results, thus interfering with the scientific validity and accuracy of tire quality grading.

[0004] Existing technologies propose averaging multiple sets of measurement data to remove the eccentricity of the spindle system in the tire uniformity testing device. However, this method completely ignores the interference of the load wheel's geometric non-circular error on the test results, failing to fundamentally solve the signal ambiguity problem. To address the interference from load wheel geometric error in tire uniformity testing, a correction method has been proposed: adding a rotating phase meter to the outside of the load wheel. By real-time acquisition and Fourier transform processing of the load wheel's rotation phase and tire radial force data, the influence of load wheel error on the data can be removed. However, the introduction of additional testing equipment not only significantly increases the hardware purchase cost of the testing system but also increases the complexity of equipment installation, commissioning, and subsequent maintenance, making it difficult to meet the high-efficiency and low-cost requirements of large-scale industrial testing. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, this invention provides a method and system for correcting tire uniformity detection signals. Through a purely data-driven approach, it achieves time-domain identification and precise separation of the geometric error of the loaded wheel in the original tire uniformity detection signal. This eliminates the need for additional hardware, significantly reducing data processing complexity and the cost of the detection system, and improving the accuracy of tire uniformity detection.

[0006] In a first aspect, the present invention provides a method for correcting tire uniformity detection signals.

[0007] A method for correcting tire uniformity detection signals includes:

[0008] Collect the original detection signal of the tire during the uniformity detection process, and calculate the number of sampling points for one revolution of the tire and the load wheel;

[0009] Based on the determined number of sampling points, a model of the original detection signal is constructed, which is formed by the superposition of tire error signal, load wheel error signal and DC bias. The unknown superposition components in the model are formed into vector form, and a matrix is ​​constructed to establish a system of linear equations.

[0010] Based on the original detection signal, the initial periodic reference waveforms of the tire error signal and the load wheel error signal are calculated using the periodic synchronous averaging method. Using the initial periodic reference waveform as the initial value, the linear equation system is solved alternately and iteratively using the least squares method until the iteration converges. The load wheel error signal and the tire error signal after separating the load wheel geometric error are then output.

[0011] A further technical solution, the determination of the number of sampling points for one revolution of the tire and load wheel, includes:

[0012] The number of sampling points per revolution of the tire is determined based on the pulse signal output by the rotary encoder on the tire detection spindle during tire uniformity testing.

[0013] Based on the number of sampling points for one revolution of the tire, combined with the distance between the load wheel and the tire axle and the radius of the load wheel, the number of sampling points for one revolution of the load wheel is calculated.

[0014] A further technical solution is that the original detection signal model is:

[0015] ;

[0016] in, This represents the original detection signal, n=0, 1, 2, ..., N-1, where N is the number of sampling points of the original detection signal; This is the periodic reference waveform for the tire error signal. This represents the tire rotation cycle, i.e., the number of sampling points per revolution of the tire axle, k=0, 1, 2, ..., -1; This is the periodic reference waveform for the load wheel error signal. The rotation period of the load wheel is the number of sampling points per revolution of the load wheel shaft, k=0, 1, 2, ..., -1; For the remainder calculation, i.e., the remainder when n is divided by N; c is the DC bias component, representing the fundamental value of the signal.

[0017] A further technical solution involves calculating the initial periodic reference waveforms of the tire error signal and the load wheel error signal using the periodic synchronous averaging method based on the original detection signals, including:

[0018] The original detection signal was sampled at the number of points per revolution of the tire. The system is segmented, all complete periodic segments are extracted and aligned, and the average of the aligned segments is calculated to obtain the initial periodic reference waveform of the tire error signal.

[0019] The initial periodic reference waveform of the tire error signal is subtracted from the original detection signal to obtain the initial load wheel error signal. Then, the initial load wheel error signal is calculated based on the number of sampling points per revolution of the load wheel. The system is segmented, and the optimal phase offset of each segment is found and aligned through cross-correlation analysis. Then, the average of the aligned segments is calculated to obtain the initial periodic reference waveform of the load wheel error signal.

[0020] A further technical solution involves finding and aligning the optimal phase offset for each segment through cross-correlation analysis, as follows:

[0021] For the initial error signal of the load wheel after segmentation, the first segment signal is used as the reference segment. The cross-correlation function between the remaining segment signals and the reference segment is calculated, and the time shift that maximizes the cross-correlation function is found, i.e., the optimal phase shift.

[0022] Based on the optimal time shift, the phase of each segment signal is aligned with the reference segment.

[0023] A further technical solution employs the least squares method to iteratively optimize and solve the linear equation system. The process is as follows:

[0024] The tire and load wheel error signals are estimated by periodically extending the initial periodic reference waveforms of the tire and load wheel error signals.

[0025] The estimation of the tire error signal under fixed load is obtained by subtracting the estimated signal from the original detection signal to obtain the residual of the tire error signal. Then, combined with the constructed matrix containing only the tire error signal and DC bias, the corrected tire error signal and DC bias estimate are obtained by solving the least squares method.

[0026] The estimation of the fixed-correction tire error signal is obtained by subtracting the estimated signal from the original detection signal to obtain the residual of the load wheel error signal. Then, combined with the constructed matrix containing only the load wheel error signal and DC bias, the estimation of the corrected load wheel error signal and DC bias is obtained by least squares solution.

[0027] Based on the total DC bias of the tire and load wheel error signals, the DC bias is allocated according to the ratio of their signal power, and the reallocated DC bias is used to correct the estimation of the tire error signal and the load wheel error signal.

[0028] Calculate the correction error of the current iteration. If the rate of change of error is less than the set convergence threshold, stop the iteration and output the finally corrected tire error signal and load wheel error signal. Otherwise, use the output signal of the current iteration as input and continue to iterate until the maximum number of iterations is reached.

[0029] Secondly, the present invention provides a tire uniformity detection signal correction system.

[0030] A tire uniformity detection signal correction system, comprising:

[0031] The data acquisition and preprocessing module is used to acquire the original detection signals of the tire during the uniformity detection process and calculate the number of sampling points for one revolution of the tire and the load wheel.

[0032] The model building module is used to construct the original detection signal model formed by the superposition of tire error signal, load wheel error signal and DC bias according to the determined number of sampling points. It constructs the unknown superposition components in the model into vector form and builds matrices to establish a system of linear equations.

[0033] The signal solving and correction module is used to calculate the initial periodic reference waveforms of the tire error signal and the load wheel error signal based on the original detection signal using the periodic synchronous averaging method. Using the initial periodic reference waveform as the initial value, the least squares method is used to alternately optimize and iterate the linear equation system until the iteration converges, and outputs the load wheel error signal and the tire error signal after separating the load wheel geometric error.

[0034] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-described tire uniformity detection signal correction method when executing the executable instructions stored in the memory.

[0035] Fourthly, the present invention also provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-described tire uniformity detection signal correction method.

[0036] Fifthly, the present invention also provides a computer program product comprising executable instructions stored in a computer-readable storage medium; wherein, when the processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned tire uniformity detection signal correction method is implemented.

[0037] The above one or more technical solutions have the following beneficial effects:

[0038] This invention provides a method and system for correcting tire uniformity detection signals. Employing a purely data-driven approach, it establishes a superposition model of the tire uniformity detection signals by constructing a matrix. Using least squares to solve for the periodic reference waveform, and through iterative optimization, it accurately separates the geometric error signals of the loaded wheel, effectively eliminating the interference of load wheel errors on the tire uniformity detection results and improving the accuracy of tire quality grading. The entire process eliminates the need for additional detection equipment such as rotating phase meters, significantly reducing the hardware cost and installation / maintenance complexity of the detection system. Furthermore, the data processing is simple, using a periodic synchronous averaging method to obtain the initial reference waveform. The alternating optimization iterative process converges quickly, reducing data processing complexity and making it suitable for efficient applications in practical detection scenarios.

[0039] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0040] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0041] Figure 1 This is an overall flowchart of the tire uniformity detection signal correction method in Embodiment 1 of the present invention;

[0042] Figure 2 This is a flowchart of the alternating optimization solution in Embodiment 1 of the present invention;

[0043] Figure 3 This is a schematic diagram of tire uniformity detection in Embodiment 1 of the present invention;

[0044] Figure 4 This is a schematic diagram showing the relationship between the load wheel and the tire distance in Embodiment 1 of the present invention;

[0045] Figure 5 This is a schematic diagram of the data signal waveforms sampled and generated in Embodiment 1 of the present invention; wherein, (a) is the original detection signal waveform sampled, (b) is the separated load wheel error signal waveform, and (c) is the tire error waveform after separating the geometric error of the load wheel.

[0046] The components include: 1. tire; 2. main shaft; 3. rotary encoder; and 4. load wheel. Detailed Implementation

[0047] It should be noted that the following detailed descriptions are exemplary and are intended only to describe specific embodiments and to provide further explanation of the invention, and are not intended to limit the scope of exemplary embodiments of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0048] Example 1

[0049] This embodiment provides a method for correcting tire uniformity detection signals, such as... Figure 1 As shown, the specific steps include:

[0050] Step S1: Collect the original detection signal of the tire during the uniformity detection process, and calculate the number of sampling points for one revolution of the tire and the load wheel;

[0051] Step S2: Based on the determined number of sampling points, construct the original detection signal model formed by the superposition of tire error signal, load wheel error signal and DC bias. Construct vector form for each unknown superposition component in the model and construct a matrix to establish a system of linear equations.

[0052] Step S3: Based on the original detection signal, the initial periodic reference waveforms of the tire error signal and the load wheel error signal are calculated using the periodic synchronous averaging method. Using the initial periodic reference waveform as the initial value, the linear equation system is alternately optimized and iterated using the least squares method until the iteration converges, and the load wheel error signal and the tire error signal after separating the load wheel geometric error are output.

[0053] The tire uniformity detection signal correction method proposed in this embodiment will be described in more detail below.

[0054] In step S1, the uniformity of the tire is detected, the raw data signals during the detection process are collected, and the signal cycles of the tire and the load wheel are calculated.

[0055] like Figure 3As shown, a uniformity test is performed on a tire. Tire 1 is mounted on a main shaft 2, and a rotary encoder 3 is mounted on the main shaft 2. Force sensors of the uniformity testing device are installed at the upper and lower ends of the main shaft of the load wheel 4. The load wheel contacts and squeezes the tire to reach a certain load, driving the tire to perform the test. The load wheel 4 and tire 1 are squeezed and undergo pure rolling friction. The rotation of the tire drives the load wheel to rotate synchronously. The tire's non-uniformity (such as out-of-roundness, thickness deviation, etc.) and the geometric errors of the load wheel (such as surface unevenness, roundness deviation, etc.) will act together at the contact point. The force sensor detects this interaction force and converts it into a measurable voltage signal. This original detection signal of the tire during the uniformity testing process is collected. The horizontal axis of this data signal waveform is the number of sampling points, and the vertical axis is the collected sensor voltage value.

[0056] like Figure 4 As shown, the magnitude of the load on the tire caused by the load wheel compressing it is related to the distance between the load wheel spindle and the tire spindle. Given that the radius of the load wheel is R, and the linear velocities of the tire and the load wheel are equal, the radius r of the tire during the testing process can be calculated as follows:

[0057] ;

[0058] In actual testing, the tire is mounted on a testing spindle, which is equipped with a rotary encoder. Based on the pulse signal output by the rotary encoder on the tire testing spindle during tire uniformity testing, the number of sampling points for one revolution of the tire can be determined. Let N1 be the number of sampling data points for one revolution of the tire, and N2 be the number of data points mapped onto the tire sampling data for one revolution of the load wheel. The relationship is as follows:

[0059] ;

[0060] Therefore, based on the number of sampling points during one revolution of the tire, combined with the distance between the load wheel and the tire's main shaft and the radius of the load wheel, the number of sampling points during one revolution of the load wheel can be calculated as follows:

[0061] ;

[0062] In the above formula, This indicates rounding down to the nearest integer.

[0063] In step S2, the original detection signal model is constructed and converted into a least squares data model, laying the foundation for subsequent data processing.

[0064] Specifically, tire uniformity detection yields a raw detection data signal x[n] with N samples, n=0, 1, 2,..., N-1. This signal is obtained by superimposing the tire detection signal (or tire error signal), the load wheel error signal (or load wheel geometric error signal), and a DC bias. The model of this raw detection signal can be expressed as:

[0065] ;

[0066] in, This is the periodic reference waveform for the tire error signal, with a period of N1 and k = 0, 1, 2, ..., N1-1; The periodic reference waveform for the load wheel error signal is N2, with k=0, 1, 2, ..., N2-1; For the remainder calculation, i.e., the remainder when n is divided by N; c is the DC bias component, representing the fundamental value of the signal.

[0067] Based on the above model, the unknown parameters (i.e., each superimposed component) are organized into a vector form as follows:

[0068] ;

[0069] in, It is the periodic reference waveform vector of the tire error signal, with dimension . ; It is the periodic reference waveform vector of the load wheel error signal, with dimension . c is a DC bias scalar.

[0070] Therefore, vector The dimension is .

[0071] To establish a system of linear equations, construct matrix A, whose size is... Each row of matrix A corresponds to a sampling point, and each column corresponds to an unknown parameter. For the (n+1)th row (corresponding to sampling point n), the matrix elements are defined as follows:

[0072] ;

[0073] Where row index n+1 corresponds to sampling point n, ; Column index j corresponds to the index of the unknown parameter, ; The phase index position corresponding to the tire error signal is incremented by 1 because column indices start from 1, while the modulo operation result starts from 0, i.e., from column 1 to column 2. List; The phase index position corresponding to the load wheel error signal, offset by the tire error signal. Column, i.e., the first Listed to number List; The position corresponding to DC bias c, i.e., the last column List.

[0074] Furthermore, a system of linear equations can be formed, expressed as:

[0075] ;

[0076] in, It is sampled data, with a dimension of N×1.

[0077] In step S3, the least squares method is used to perform alternating optimization and iterative solutions to the linear equation system.

[0078] Step S3.1: Calculate the initial reference waveform of the signal. That is, since directly solving a large-scale linear system may be unstable, the initial reference waveform of the signal is first obtained through the periodic synchronous averaging method, which serves as the starting point for alternating optimization.

[0079] Step S3.1.1: Based on the original detection signal, calculate the initial periodic reference waveform of the tire error signal using the periodic synchronous averaging method. Specifically, the original detection signal is calculated based on the number of sampling points per revolution of the tire. The system is segmented, all complete periodic segments are extracted and aligned, and the average of the aligned segments is calculated to obtain the initial periodic reference waveform of the tire error signal.

[0080] In this embodiment, the original detected tire signal is directly segmented and aligned, that is, the signal in each segment is aligned. Each sampling point is sequentially aligned, and then the data with the same number of sampling points in each segment are averaged. The initial periodic reference waveform of the tire error signal, estimated by aligning and averaging each period, can be expressed as:

[0081] ;

[0082] In the above formula, This is the index of the sampling points within the period; The number of complete cycles of the tire signal. Indicates rounding down to the nearest integer; To calculate the absolute position in the original signal, when k ranges from 0 to... When changes occur, all points in the m-th period can be retrieved.

[0083] Step S3.1.2: Based on the original detection signal, the initial periodic reference waveform of the load wheel error signal is calculated using the periodic synchronous averaging method. Specifically, firstly, the initial periodic reference waveform of the tire error signal is subtracted from the original detection signal to obtain the initial load wheel error signal. Then, the initial load wheel error signal is calculated based on the number of sampling points per revolution of the load wheel. Segmentation can be represented as:

[0084] ;

[0085] In the above formula, , This represents the number of complete cycles of the load wheel signal.

[0086] Then, the optimal phase offset for each segment is found and aligned through cross-correlation analysis. The average of the aligned segments is then calculated to obtain the initial periodic reference waveform of the load wheel error signal. In this embodiment, for the segmented initial load wheel error signal, the first segment signal (m=0) is used as the reference segment. The cross-correlation function between the remaining segment signals and the reference segment is calculated, which can be expressed as:

[0087] ;

[0088] in, , The period length is denoted as .

[0089] Find the time shift that maximizes the cross-correlation function, i.e., the optimal phase shift, which is:

[0090] ;

[0091] In the above formula, Represents the cross-correlation function Time shift to obtain the maximum value This refers to the optimal offset for achieving phase alignment between two signal segments.

[0092] Then, based on the optimal time shift, the phase of each segmented signal is aligned with the reference segment as follows:

[0093] .

[0094] By solving for the optimal time shift to achieve phase alignment, the unknown initial phase offset of each segment can be automatically compensated. This ensures that when performing averaging, the k-th point of all segments corresponds to the same physical phase position on the periodic waveform of the signal. The disordered periodic signal components scattered in different segments are integrated in phase, which enables the subsequent averaging to effectively enhance the weak signal characteristics of the load wheel, avoid waveform distortion, and provide an accurate initial periodic reference waveform for subsequent alternation optimization.

[0095] Finally, the average of all aligned segments is calculated to obtain the initial periodic reference waveform of the load wheel error signal, which is:

[0096] .

[0097] Step S3.2: After obtaining the initial estimate, the least squares method is used to iteratively optimize and solve the linear equation system, such as... Figure 2 As shown, the specific steps include:

[0098] Step S3.2.1: Let the current iteration number be... i and order and The error signals for the tire and the load wheel are respectively based on the initial period reference waveform. and Signal estimation obtained by periodic extension.

[0099] Step S3.2.2: Estimation of the fixed load wheel error signal. Subtract the estimated signal from the original detection signal to obtain the residual of the tire error signal. Then, combine the constructed matrix containing only the tire error signal and DC bias, and solve it by least squares to obtain the corrected tire error signal and DC bias estimate.

[0100] Specifically, the current estimation of the fixed load wheel error signal Subtracting the estimated value of the tire error signal from the detected sampled signal yields the residual of the tire error signal, which is:

[0101] ;

[0102] Then, the least squares solution is performed, resulting in:

[0103] ;

[0104] In the above formula, It is a matrix containing only tire error signals and DC bias, with a size of Let be a submatrix of matrix A. For the (n+1)th row:

[0105] ;

[0106] In the above formula, , It is the periodic reference waveform vector of the tire error signal, with dimensions... , It is the DC bias of the tire error signal.

[0107] The least squares solution is:

[0108] ;

[0109] Therefore, the waveform of the corrected tire error signal is as follows:

[0110] .

[0111] Step S3.2.3: Estimation of the fixed correction tire error signal. Subtract the estimated signal from the original detection signal to obtain the residual of the load wheel error signal. Then, combine the constructed matrix that contains only the load wheel error signal and DC bias, and obtain the estimated corrected load wheel error signal and DC bias by least squares solution.

[0112] Specifically, the updated estimation of the fixed tire error signal Subtracting the estimated value of the signal from the detected sampled signal yields the residual of the load wheel error signal, which is:

[0113] ;

[0114] Then, the least squares solution is performed, resulting in:

[0115] ;

[0116] In the above formula, It is a matrix containing only the load wheel error signal and DC bias, with a size of Let be a submatrix of matrix A. For the (n+1)th row:

[0117] ;

[0118] In the above formula, , It is the periodic reference waveform vector of the load wheel error signal. It is the DC bias of the load wheel error signal.

[0119] The least squares solution is:

[0120] ;

[0121] Therefore, the waveform of the corrected load wheel error signal is as follows:

[0122] .

[0123] Step S3.2.4: Based on the total DC bias of the tire and load wheel error signals, allocate the DC bias according to the power ratio of the two signals, and use the reallocated DC bias to correct the estimation of the tire error signal and the load wheel error signal.

[0124] In each iteration, two independent DC bias estimates are obtained. and However, in the actual sampled data, there is only one DC bias, which can be expressed as:

[0125] ;

[0126] Based on the power ratio of the two signals, the DC bias is redistributed, and the power of the two signals is calculated as follows:

[0127] ;

[0128] ;

[0129] in, The L2 norm of a vector is represented by the square root of the sum of the squares of its elements.

[0130] Therefore, the allocation coefficient is calculated as follows:

[0131] ;

[0132] ;

[0133] The DC bias is redistributed as follows:

[0134] ;

[0135] ;

[0136] Next, the waveforms of the two signals are corrected using a reallocated DC bias, as follows:

[0137] ;

[0138] .

[0139] Step S3.2.5: Calculate the correction error of the current iteration. If the error change rate is less than the set convergence threshold, stop the iteration and output the finally corrected tire error signal and load wheel error signal. Otherwise, use the output signal of the current iteration as input and continue to perform cyclic iteration until the maximum number of iterations is reached.

[0140] Specifically, the correction error for the current iteration is calculated as follows:

[0141] ;

[0142] If the following convergence condition is met, then the iteration stops:

[0143] ；

[0144] In the above formula, Let be the preset convergence threshold. If the convergence condition is not met, then let . i=i +1, return to continue iteration. Simultaneously, set a maximum number of iterations; if the maximum number of iterations is reached, stop iterating to avoid infinite loops.

[0145] Finally, after satisfying the iterative convergence condition, the load wheel error signal is obtained as follows:

[0146] ;

[0147] The tire error signal is:

[0148] ;

[0149] In the above formula, This is the DC bias allocated to the load wheel after final convergence.

[0150] Through the above method, this embodiment can achieve temporal identification and accurate separation of load wheel geometric error in the original tire uniformity detection signal using a purely data-driven approach.

[0151] Furthermore, to verify the superiority of the method proposed in this embodiment, a practical testing condition is used as an example. In this example, 512 data points are collected for one revolution of the tire, and for 5 revolutions, a total of N=2560 data points are collected. Therefore, the number of sampling points for one revolution of the tire is N1=512. The diameter of the load wheel is known to be 854mm, i.e., R=427mm. Taking one tire as an example, the distance between the load wheel spindle and the tire spindle is L=743.3mm to provide the load required for detection. Therefore, the number of sampling points corresponding to one revolution of the load wheel can be calculated as N2=691. In addition, the tire uniformity detection collects the voltage value of the sensor during sampling. The horizontal axis of the waveform graph is the number of sampling points, and the vertical axis is the collected voltage value of the sensor. The sampling data signal waveform graph is the original data waveform obtained from the tire uniformity detection. Data is collected for 5 revolutions of the tire, as shown in the figure. Figure 5 As shown in (a), the circled waveform shows that due to the influence of the load wheel's geometric error, the amplitude of the signal waveform in these 5 cycles of the tire error signal is significantly different.

[0152] The signal waveform of the load wheel geometric error is obtained after calculation and processing by the above algorithm, as follows: Figure 5 As shown in (b), the waveform of the separated load wheel error signal also shows a period of 691, which is equal to the number of sampling points N2 for one revolution of the load wheel. The final waveform of the tire error signal influenced by the geometric error of the separated load wheel is as follows: Figure 5 As shown in (c), it can be seen that the waveforms of the five cycles of the signal are the same, and the waveform portion affected by the load wheel error has been corrected.

[0153] Example 2

[0154] This embodiment provides a tire uniformity detection signal correction system, including:

[0155] The data acquisition and preprocessing module is used to acquire the original detection signals of the tire during the uniformity detection process and calculate the number of sampling points for one revolution of the tire and the load wheel.

[0156] The model building module is used to construct the original detection signal model formed by the superposition of tire error signal, load wheel error signal and DC bias according to the determined number of sampling points. It constructs the unknown superposition components in the model into vector form and builds matrices to establish a system of linear equations.

[0157] The signal solving and correction module is used to calculate the initial periodic reference waveforms of the tire error signal and the load wheel error signal based on the original detection signal using the periodic synchronous averaging method. Using the initial periodic reference waveform as the initial value, the least squares method is used to alternately optimize and iterate the linear equation system until the iteration converges, and outputs the load wheel error signal and the tire error signal after separating the load wheel geometric error.

[0158] Example 3

[0159] This embodiment provides an electronic device, including: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the method provided in this embodiment.

[0160] Example 4

[0161] This embodiment also provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, will cause the processor to execute the method described above in this embodiment.

[0162] Example 5

[0163] This embodiment provides a computer program product including executable instructions, which are computer instructions; the executable instructions are stored in a computer-readable storage medium. When the processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the electronic device performs the method described in this embodiment.

[0164] The steps and methods involved in Embodiments 2 to 5 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0165] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0166] The above description is only a preferred embodiment of the present invention. Although the specific implementation of the present invention has been described in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative effort are still within the scope of protection of the present invention.

Claims

1. A method for correcting tire uniformity detection signals, characterized in that, include: Collect the original detection signal of the tire during the uniformity detection process, and calculate the number of sampling points for one revolution of the tire and the load wheel; Based on the determined number of sampling points, an original detection signal model is constructed by superimposing the periodic reference waveform of the tire error signal, the periodic reference waveform of the load wheel error signal, and the DC bias. The unknown superimposed components in the model are formed into vector form, and matrices are constructed to establish a system of linear equations. Based on the original detection signals, the initial periodic reference waveforms of the tire error signal and the load wheel error signal are calculated using the periodic synchronous averaging method, including: The original detection signal is segmented according to the number of sampling points per revolution of the tire. All complete period segments are extracted and aligned. The average of the aligned segments is calculated to obtain the initial period reference waveform of the tire error signal. The initial periodic reference waveform of the tire error signal is obtained by subtracting the original detection signal from the initial periodic reference waveform of the tire error signal. The initial periodic reference waveform of the load wheel error signal is then divided into segments according to the number of sampling points for one revolution of the load wheel. The optimal phase offset of each segment is found and aligned through cross-correlation analysis. The average of the aligned segments is then calculated to obtain the initial periodic reference waveform of the load wheel error signal. Based on the aforementioned model, the unknown superposition components are organized into vector form as follows: ； in, It is the periodic reference waveform vector of the tire error signal, with dimension . ; It is the periodic reference waveform vector of the load wheel error signal, with dimension . c is the DC bias scalar; vector The dimension is ; To establish a system of linear equations, construct matrix A, whose size is... Each row of matrix A corresponds to a sampling point, and each column corresponds to an unknown parameter. For the (n+1)th row, the matrix elements are defined as follows: ； Where row index n+1 corresponds to sampling point n, ; Column index j corresponds to the index of the unknown parameter, ; The column index corresponds to the phase index position of the tire error signal, starting from 1, while the modulo operation result starts from 0, i.e., from column 1 to column 2. List; The phase index position corresponding to the load wheel error signal, offset by the tire error signal. Column, i.e., the first Listed to number List; The position corresponding to DC bias c, i.e., the last column List; This forms a system of linear equations, expressed as: ； in, It is sampled data with a dimension of N×1; Based on the original detection signal, the initial periodic reference waveforms of the tire error signal and the load wheel error signal are calculated using the periodic synchronous averaging method. Using the initial periodic reference waveforms as initial values, the tire error signal and the load wheel error signal in the linear equation system are alternately optimized and iterated using the least squares method until the iteration converges. The load wheel error signal and the tire error signal after separating the load wheel geometric error are then output.

2. The tire uniformity detection signal correction method as described in claim 1, characterized in that, The calculation determines the number of sampling points for one revolution of the tire and load wheel, including: The number of sampling points per revolution of the tire is determined based on the pulse signal output by the rotary encoder on the tire detection spindle during tire uniformity testing. The number of sampling points for one revolution of the load wheel is calculated based on the number of sampling points for one revolution of the load wheel, combined with the distance between the load wheel and the tire axle and the radius of the load wheel.

3. The tire uniformity detection signal correction method as described in claim 1, characterized in that, The original detection signal model is as follows: ； in, This represents the original detection signal, n=0, 1, 2, ..., N-1, where N is the number of sampling points of the original detection signal; This is the periodic reference waveform for the tire error signal. This represents the tire rotation cycle, i.e., the number of sampling points per revolution of the tire axle, k=0, 1, 2, ..., -1; This is the periodic reference waveform for the load wheel error signal. The rotation period of the load wheel is the number of sampling points per revolution of the load wheel shaft, k=0, 1, 2, ..., -1; For the remainder calculation, i.e., the remainder when n is divided by N; c is the DC bias component, representing the fundamental value of the signal.

4. The tire uniformity detection signal correction method as described in claim 1, characterized in that, The optimal phase offset for each segment was found and aligned using cross-correlation analysis, as follows: For the initial error signal of the load wheel after segmentation, the first segment signal is used as the reference segment. The cross-correlation function between the remaining segment signals and the reference segment is calculated, and the time shift that maximizes the cross-correlation function is found, i.e., the optimal phase shift. Based on the optimal time shift, the phase of each segment signal is aligned with the reference segment.

5. The tire uniformity detection signal correction method as described in claim 1, characterized in that, The linear equation system is solved iteratively using the least squares method with alternating optimization. The process is as follows: The tire and load wheel error signals are estimated by periodically extending the initial periodic reference waveforms of the tire and load wheel error signals. The estimation of the fixed-load wheel error signal involves subtracting the estimated signal from the original detected signal to obtain the residual of the tire error signal. This residual is then combined with a constructed matrix containing only the tire error signal and DC bias. The corrected tire error signal and DC bias estimates are obtained by solving the matrix using the least squares method. Specifically, the current estimate of the fixed-load wheel error signal... Subtracting the estimated value of the tire error signal from the detected sampled signal yields the residual of the tire error signal, which is: ； Then, the least squares solution is performed, resulting in: ； In the above formula, It is a matrix containing only tire error signals and DC bias, with a size of Let be a submatrix of matrix A. For the (n+1)th row: ； In the above formula, , It is the periodic reference waveform vector of the tire error signal, with dimensions... , It is the DC bias of the tire error signal; The estimation of the fixed-correction tire error signal involves subtracting the estimated signal from the original detection signal to obtain the residual of the load wheel error signal. This residual is then combined with a constructed matrix containing only the load wheel error signal and DC bias. Least squares are then used to obtain the corrected estimates of the load wheel error signal and DC bias. Specifically, this involves updating the estimation of the fixed tire error signal. Subtracting the estimated value of the signal from the detected sampled signal yields the residual of the load wheel error signal, which is: ； Then, the least squares solution is performed, resulting in: ； In the above formula, It is a matrix containing only the load wheel error signal and DC bias, with a size of Let be a submatrix of matrix A. For the (n+1)th row: ； In the above formula, , It is the periodic reference waveform vector of the load wheel error signal. It is the DC bias of the load wheel error signal; Based on the total DC bias of the tire and load wheel error signals, the DC bias is allocated according to the ratio of their signal power, and the reallocated DC bias is used to correct the estimation of the tire error signal and the load wheel error signal. Calculate the correction error of the current iteration. If the rate of change of error is less than the set convergence threshold, stop the iteration and output the finally corrected tire error signal and load wheel error signal. Otherwise, use the output signal of the current iteration as input and continue to iterate until the maximum number of iterations is reached.

6. A tire uniformity detection signal correction system, characterized in that, include: The data acquisition and preprocessing module is used to acquire the original detection signals of the tire during the uniformity detection process and calculate the number of sampling points for one revolution of the tire and the load wheel. The model building module is used to construct the original detection signal model based on the determined number of sampling points. It consists of the periodic reference waveform of the tire error signal, the periodic reference waveform of the load wheel error signal, and the DC bias superposition. The unknown superposition components in the model are converted into vector form, and matrices are constructed to establish a system of linear equations. Based on the original detection signals, the initial periodic reference waveforms of the tire error signal and the load wheel error signal are calculated using the periodic synchronous averaging method, including: The original detection signal is segmented according to the number of sampling points per revolution of the tire. All complete period segments are extracted and aligned. The average of the aligned segments is calculated to obtain the initial period reference waveform of the tire error signal. The initial periodic reference waveform of the tire error signal is obtained by subtracting the original detection signal from the initial periodic reference waveform of the tire error signal. The initial periodic reference waveform of the load wheel error signal is then divided into segments according to the number of sampling points for one revolution of the load wheel. The optimal phase offset of each segment is found and aligned through cross-correlation analysis. The average of the aligned segments is then calculated to obtain the initial periodic reference waveform of the load wheel error signal. Based on the aforementioned model, the unknown superposition components are organized into vector form as follows: ； in, It is the periodic reference waveform vector of the tire error signal, with dimension . ; It is the periodic reference waveform vector of the load wheel error signal, with dimension . c is the DC bias scalar; vector The dimension is ; To establish a system of linear equations, construct matrix A, whose size is... Each row of matrix A corresponds to a sampling point, and each column corresponds to an unknown parameter. For the (n+1)th row, the matrix elements are defined as follows: ； Where row index n+1 corresponds to sampling point n, ; Column index j corresponds to the index of the unknown parameter, ; The column index corresponds to the phase index position of the tire error signal, starting from 1, while the modulo operation result starts from 0, i.e., from column 1 to column 2. List; The phase index position corresponding to the load wheel error signal, offset by the tire error signal. Column, i.e., the first Listed to number List; The position corresponding to DC bias c, i.e., the last column List; This forms a system of linear equations, expressed as: ； in, It is sampled data with a dimension of N×1; The signal solving and correction module is used to calculate the initial periodic reference waveforms of the tire error signal and the load wheel error signal based on the original detection signal using the periodic synchronous averaging method. Using the initial periodic reference waveform as the initial value, the least squares method is used to alternately optimize and iterate the tire error signal and the load wheel error signal in the linear equation system until the iteration converges, and outputs the load wheel error signal and the tire error signal after separating the load wheel geometric error.

7. An electronic device, characterized in that, include: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the tire uniformity detection signal correction method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The device stores executable instructions that, when executed by a processor, implement the tire uniformity detection signal correction method according to any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, it implements the tire uniformity detection signal correction method according to any one of claims 1-5.

Images